Adaptive NN Tracking Control for Pure-Feedback Stochastic Nonlinear Systems Based on Dynamic Surface Control

摘要：

　　This paper focuses on the problem of adaptive neural network(NN)tracking control for a class of pure-feedback stochastic nonlinear systems.Via the dynamic surface control(DSC)technique and neural networks approximation capability,a novel adaptive NN control scheme is proposed.Without using the mean value theorem,an affine variable at each step is constructed.By introducing the additional first-order low-pass filter for the actual control input,the algebraic loop problem in pure-feedback stochastic nonlinear systems is solved.It is proved that the proposed controller ensures that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded(SGUUB)in probability while the tracking error converges to a small neighborhood of the origin.Finally,a numerical example is provided to illustrate the effectiveness of the proposed method.

关键词： Adaptive NN control Dynamic surface control Backstepping Pure-feedback stochastic nonlinear systems

作者: CUI Guozeng ZHANG Baoyong

作者单位: School of Automation,Nanjing University of Science and Technology,Nanjing 210094,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 8735-8740

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）